R workflow

[conflicted] Will prefer haven::is.labelled over any other package.
[conflicted] Will prefer dplyr::filter over any other package.

Agenda

R & RStudio Workflow
Quarto
Getting Started

Learning objectives

By the end of the lab, you will be able to …

setup a reproducible workflow using R and RStudio
familiarize yourself with a dataset using R and RStudio
create a reproducible report using Quarto

R & RStudio Workflow

Replication

The guiding principle for workflow.

A workflow of data analysis is a process for managing all aspects of data analysis.

Planning, documenting, and organizing your work; cleaning the data; creating, renaming, and verifying variables; performing and presenting statistical analyses; producing replicable results; and archiving what you have done are all integral parts of your workflow.

Source: (Long 2008)

Steps in a workflow

Set up	Systematic organization of the project and project files.
Familiarize self with data	Skipping takes more time in the long run.
Process data	Takes the MOST time.
Running analyses	What people THINK takes the most time.
Presenting results	What people (wrongly) think does not take time.

File types

There are many file types, but these are key to an R & RStudio workflow (and likely new to you):

Extension	Description
.Rproj	RStudio project file (keeps project settings).
.R	R scripts store a sequence of R commands (code) that can be run all at once or line by line.
.qmd	Quarto Markdown creates reproducible documents that contain a combination of text, code, and output.
.Rdata (or sometimes .rda)	These store and load R objects—like data frames.

File names

should be:

machine-readable
human-readable
play well with default-ordering

RStudio projects

Create a RStudio project for each data analysis project.

It supports an organized and reproducible workflow, cleanly separated from all other projects that you are working on. Everything you need in one place:

local data files to load into RStudio.
scripts to edit or run in bits or as a whole.
Save your outputs (plots and cleaned data).

Source: https://docs.posit.co/ide/user/ide/get-started/#hello-rstudio-projects

Filepaths

Adopting a project-based workflow avoids changing file paths.

ABSOLUTE FILE PATHS

Department of Sociology
Unit 17100, 17th Floor, Ontario Power Building
700 University Ave., Toronto, ON M5G 1Z5

C:\Users\Pepin\GitHub\SOC6302\scripts

RELATIVE FILE PATHS

Take the left side elevators to the 17th floor.
Go through the double doors and a take a right.
First door on your left.

here(scripts)

Tour: RStudio Panes

Sit back and enjoy the show!

open R script or Quarto document
Environment (data values, functions)
Plots, Help, Viewer
Console – typing

Tour recap: Panes

There are four key regions or “panes” in the interface:

Source pane: where you can edit and save R scripts or author computational documents like Quarto and R Markdown.
Console pane: is used to write short interactive R commands.
Environment pane: displays temporary R objects created during that R session.
Output pane: displays the plots, tables, or HTML outputs of executed code along with files saved to disk.

Heads Up!

The top-left panel (source pane) and can be launched by opening any editable file in RStudio.

Console pane: best for exploring.
Source pane: best for documenting.

R-scripts and Quarto

Open RStudio, then click the dropdown arrow next to the “New File icon,” and then “R script” or “Quarto Document.”

Alternatively, hold down “Ctrl” + “Shift” + “N.”

An R-script is a file that will contain the documentation of the code of what you tell R to do.

Blank slate

Clear the memory at every restart of RStudio by turning off the automatic saving of your workspace and .Rdata files with you quit RStudio. This is important for reproducibility, debugging, and avoiding littering your computer with unnecessary files.

Set this via:

Tools > Global Options.
Uncheck “Restore .RData into Workspace at Startup”.
Choose “Never” on the “Save workspace to .RData on exit”.
Click “Apply” and “OK”.

Comprehensive R Archive Network (CRAN)

CRAN is like an App Store for R. It hosts R packages, documentation, and source code contributed by users worldwide. It is mediated (e.g., quality controlled), making it incredibly reliable.

R users can easily install, update, and share R packages using install.packages().

Packages

R comes with basic tools, but packages extend the capabilities of base R (what you already installed). An R package is like a toolbox: a collection of functions, data, and documentation that help you do specific tasks using R.

You’ll install each package (only once per system):

install.packages("tidyverse")

Warning: package 'tidyverse' is in use and will not be installed

You’ll load each package (every time you use it):

library(tidyverse)

Support

Some help videos and further explanation:

R - Intro RStudio Interface

EasyR - Getting started with R the easy way

Quarto

The tool you’ll use to create reproducible computational documents. Every piece of assignment you hand in will be a Quarto document.

Fully reproducible reports
R code + narrative

You are likely familiar with word processors like MS Word or Google Docs. We will not be using these in this class. Instead, the words you would write in such a document, as well as your R code, will go into a Quarto document. You will render the document (more on what this means later) to get a document out that has your words, code, and the output of that code. Everything in one place, beautifully formatted!

RScript

great for learning, exploring and tinkering.

rerun it without attention to formatting or markdown.

Quarto

great for communicating analysis and results

combines narrative explanation with code output (results.

Documentation

In .R scripts and Quarto, you can document your code. Err on the side of over documentation. Your future self will thank you. In .R scripts, the way to make a comment rather than a command, is to put a pound sign in front of the text.

Tour: Quarto

Sit back and enjoy the show!

YAML Ain’t Markup Language
code chunk (+ code comments)
narrative (heading levels)
render
run (line vs code chunk)
source and visual

Tour recap: Quarto

Source: Dr. Mine Çetinkaya-Rundel

Tour recap: Quarto Code-chunks

chunk labels are helpful for describing what the code is doing, for jumping between code cells in the editor, and for troubleshooting
message: false hides any messages emitted by the code in your rendered document

Source: Dr. Mine Çetinkaya-Rundel

How will we use Quarto?

Every code-along and milestone will be a Quarto document
The scaffolding will decrease over the course
You will create and submit a Quarto document for your research project

Getting Started

Create a RStudio Project

To create a new project in RStudio, click: File > New Project.

In the New Project wizard that pops up, select: New Directory, then New Project.

Name the project “SOC6302” and click: Create Project.

This will launch you into a new RStudio Project inside a new folder called “SOC6302”.

Your first code-along

Download and open code-along-01.qmd

Packages

We’ll use the following packages:

here() (relative file paths)
tidyverse() (data wrangling)
gssr() (U.S. General Social Survey data)
gssrdoc() (GSS documentation)

Install `here()` and `tidyverse()`

Let’s install the two packages that are available on CRAN.

Copy and paste the following code into your Console pane. Then hit enter.

install.packages("here")

Then, do the same to install the tidyverse package.

install.packages("tidyverse")

Install `gssr()` and `gssrdoc()`

# Install 'gssr' from 'ropensci' universe
install.packages('gssr', repos =
  c('https://kjhealy.r-universe.dev', 'https://cloud.r-project.org'))

# Also recommended: install 'gssrdoc' as well
install.packages('gssrdoc', repos =
  c('https://kjhealy.r-universe.dev', 'https://cloud.r-project.org'))

Heads Up!

R ignores text after #. These comments describe syntax.

Load the packages

library(here)
library(tidyverse)
library(gssr)
library(gssrdoc)

Environment

# software documentation
sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_Canada.utf8  LC_CTYPE=English_Canada.utf8   
[3] LC_MONETARY=English_Canada.utf8 LC_NUMERIC=C                   
[5] LC_TIME=English_Canada.utf8    

time zone: America/Toronto
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gssrdoc_0.7.0      here_1.0.1         conflicted_1.2.0   summarytools_1.1.4
 [5] flextable_0.9.7    kableExtra_1.4.0   labelled_2.14.1    haven_2.5.5       
 [9] gssr_0.7           lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1     
[13] dplyr_1.1.4        purrr_1.0.4        readr_2.1.5        tidyr_1.3.1       
[17] tibble_3.3.0       ggplot2_3.5.2      tidyverse_2.0.0   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6            xfun_0.52               htmlwidgets_1.6.4      
 [4] tzdb_0.5.0              vctrs_0.6.5             tools_4.5.1            
 [7] generics_0.1.4          curl_6.3.0              pacman_0.5.1           
[10] pkgconfig_2.0.3         data.table_1.17.6       checkmate_2.3.2        
[13] pryr_0.1.6              RColorBrewer_1.1-3      uuid_1.2-1             
[16] lifecycle_1.0.4         compiler_4.5.1          farver_2.1.2           
[19] rapportools_1.2         textshaping_1.0.1       codetools_0.2-20       
[22] fontquiver_0.2.1        fontLiberation_0.1.0    htmltools_0.5.8.1      
[25] yaml_2.3.10             pillar_1.11.0           MASS_7.3-65            
[28] openssl_2.3.3           cachem_1.1.0            magick_2.8.7           
[31] fontBitstreamVera_0.1.1 tidyselect_1.2.1        zip_2.3.3              
[34] digest_0.6.37           stringi_1.8.7           reshape2_1.4.4         
[37] pander_0.6.6            rprojroot_2.1.0         fastmap_1.2.0          
[40] grid_4.5.1              cli_3.6.5               magrittr_2.0.3         
[43] base64enc_0.1-3         withr_3.0.2             backports_1.5.0        
[46] gdtools_0.4.1           scales_1.4.0            timechange_0.3.0       
[49] rmarkdown_2.29          officer_0.6.7           matrixStats_1.5.0      
[52] askpass_1.2.1           ragg_1.4.0              hms_1.1.3              
[55] memoise_2.0.1           evaluate_1.0.4          knitr_1.50             
[58] tcltk_4.5.1             viridisLite_0.4.2       rlang_1.1.6            
[61] Rcpp_1.0.14             glue_1.8.0              xml2_1.3.8             
[64] svglite_2.1.3           rstudioapi_0.17.1       jsonlite_2.0.0         
[67] plyr_1.8.9              R6_2.6.1                systemfonts_1.2.3      
[70] fs_1.6.6

Project structure

Let’s set up your project structure using the here() package.

`here()`

First, let’s establish our project directory

# shows the file path to the root of the project
here()

Next, we’ll create folders within our project.

Example folder structure

Research Projects

project/

data/

gss7924-raw.rda

gss7924-processed.Rdata/

scripts/

clean_data.R

analyze_data.R

draft.qmd

outputs/

draft.html

figures/

plot1.png

plot2.png

readme.qmd

project.Rproj

SOC6302

SOC6302/

data/

gss7924-raw.rda

gss7924-processed.Rdata/

code-alongs/

milestones/

project/

data/

scripts/

outputs/

readme.qmd

SOC6302.Rproj

Create a folder structure

using here() and dir.create()

# Create base folders
dir.create(here("data"), recursive = TRUE)
dir.create(here("code-alongs"), recursive = TRUE)
dir.create(here("milestones"), recursive = TRUE)
dir.create(here("project"), recursive = TRUE)

Create sub-folders

using here() and dir.create()

# Create project sub-folders
dir.create(here("project", "data"), recursive = TRUE)
dir.create(here("project", "scripts"), recursive = TRUE)
dir.create(here("project", "outputs"), recursive = TRUE)

Check your work

report a list of folders and or files in the R-project folders and sub-folder.

# Your SOC6302 class folder
list.files(path = here())

# Your "Project" sub-folder
list.files(path = here("project"))

Save code-along

Save this code-along in your newly created “code-along” sub-folder.

There’s no command in the R console to save scripts or Quarto files— you use the editor’s File > Save As or Ctrl+S.

Meet your data

We’re going to use data from the U.S. General Social Survey (GSS).

Load your data

# Load the data (will appear in your Global Environment pane)
data(gss_all)

# Preview the datatable which is automatically named gss_all
gss_all

# A tibble: 75,699 × 6,867
   year         id wrkstat    hrs1        hrs2        evwork      occ   prestige
   <dbl+lbl> <dbl> <dbl+lbl>  <dbl+lbl>   <dbl+lbl>   <dbl+lbl>   <dbl> <dbl+lb>
 1 1972          1 1 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 205   50      
 2 1972          2 5 [retire… NA(i) [iap] NA(i) [iap]     1 [yes] 441   45      
 3 1972          3 2 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 270   44      
 4 1972          4 1 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap]   1   57      
 5 1972          5 7 [keepin… NA(i) [iap] NA(i) [iap]     1 [yes] 385   40      
 6 1972          6 1 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 281   49      
 7 1972          7 1 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 522   41      
 8 1972          8 1 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 314   36      
 9 1972          9 2 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 912   26      
10 1972         10 1 [workin… NA(i) [iap] NA(i) [iap] NA(i) [iap] 984   18      
# ℹ 75,689 more rows
# ℹ 6,859 more variables: wrkslf <dbl+lbl>, wrkgovt <dbl+lbl>,
#   commute <dbl+lbl>, industry <dbl+lbl>, occ80 <dbl+lbl>, prestg80 <dbl+lbl>,
#   indus80 <dbl+lbl>, indus07 <dbl+lbl>, occonet <dbl+lbl>, found <dbl+lbl>,
#   occ10 <dbl+lbl>, occindv <dbl+lbl>, occstatus <dbl+lbl>, occtag <dbl+lbl>,
#   prestg10 <dbl+lbl>, prestg105plus <dbl+lbl>, indus10 <dbl+lbl>,
#   indstatus <dbl+lbl>, indtag <dbl+lbl>, marital <dbl+lbl>, …

A “tibble” is another name for “tidy dataset,” meaning that the data is organized in structured, clear rows and columns. “(75,699 × 6,867)” means the dataset contains 75,699 rows and 6,867 columns. Commonly, in social sciences, rows are referred to as “observations” and columns as “variables.” In our case, there are 75,699 observations (e.g., respondents) and 6,867 variables.

Load GSS 2024

# Get the data only for the 2024 survey respondents
gss24 <- gss_get_yr(2024)

Fetching: https://gss.norc.org/documents/stata/2024_stata.zip

# look at the first 6 rows of the dataframe
head(gss24)

# A tibble: 6 × 639
  year        id wrkstat hrs1        hrs2        evwork      marital martype    
  <dbl+lb> <dbl> <dbl+l> <dbl+lbl>   <dbl+lbl>   <dbl+lbl>   <dbl+l> <dbl+lbl>  
1 2024         1 1 [wor…    43       NA(i) [iap] NA(i) [iap] 5 [nev… NA(i) [iap]
2 2024         2 5 [ret… NA(i) [iap] NA(i) [iap]     1 [yes] 5 [nev… NA(i) [iap]
3 2024         3 5 [ret… NA(i) [iap] NA(i) [iap]     1 [yes] 1 [mar…     1 [mar…
4 2024         4 2 [wor…    20       NA(i) [iap] NA(i) [iap] 5 [nev… NA(i) [iap]
5 2024         5 5 [ret… NA(i) [iap] NA(i) [iap]     1 [yes] 3 [div… NA(i) [iap]
6 2024         6 4 [une… NA(i) [iap] NA(i) [iap] NA(i) [iap] 1 [mar…     1 [mar…
# ℹ 631 more variables: divorce <dbl+lbl>, widowed <dbl+lbl>,
#   spwrksta <dbl+lbl>, sphrs1 <dbl+lbl>, sphrs2 <dbl+lbl>, spevwork <dbl+lbl>,
#   cowrksta <dbl+lbl>, coevwork <dbl+lbl>, cohrs1 <dbl+lbl>, cohrs2 <dbl+lbl>,
#   sibs <dbl+lbl>, childs <dbl+lbl>, age <dbl+lbl>, educ <dbl+lbl>,
#   speduc <dbl+lbl>, coeduc <dbl+lbl>, codeg <dbl+lbl>, degree <dbl+lbl>,
#   padeg <dbl+lbl>, madeg <dbl+lbl>, spdeg <dbl+lbl>, sex <dbl+lbl>,
#   race <dbl+lbl>, res16 <dbl+lbl>, reg16 <dbl+lbl>, mobile16 <dbl+lbl>, …

Browse dataframe

With your mouse, go to the environment panel (upper-right) and click on the “gss24” object. It pops up and you can browse through it.

This is often a good idea to get a first feel for the data, but only if your dataset is relatively small.

Author Naomi Wolf wrote a book about the treatment of homosexuality in 19th century England. She reported that it was punishable by death since “sodomy” charges were followed by “death recorded” in the judicial logs. However, “sodomy” referred to a host of other sexual offenses, and “death recorded” specifically means they weren’t killed because their death was recorded but not carried out.

Paul Dolan wrote a book about how marriage makes you miserable. A key part of his evidence was that in the American Time Use Survey, married people surveyed about their happiness when the spouse was present reported being happy, but when the spouse was absent they reported being unhappy. Covering up unhappiness in front of their spouse, truly miserable! But while Dolan read spouse present/absent as “spouse is in the room during questioning”, it actually meant “spouse together/separated.” He was comparing couples that were together vs. couples that were separating!

Codebook

The GSS documentation is available online in .pdf form.

The .pdfs will be useful for general overviews.

For specific variable information, it will be helpful to use the documentation you’ll load into RStudio.

# Load the codebook
data(gss_dict)

Names

To see the variables available in the dataset, use the names() command.

names(gss_all)

Heads Up!

This command is best to use with smaller datasets.

Variable documentation

For information about a specific GSS variable,
type ?varname at the console.

In the output pane, the Help tab will show the variable documentation.

Heads Up!

Replace “varname” with the name of a variable.
Example: ?meovrwrk

Variable documentation example

Warning in readLines("images/01/meovrwrk.txt"): incomplete final line found on
'images/01/meovrwrk.txt'


meovrwrk {gssrdoc}  R Documentation
Men hurt family when focus on work too much
Description
meovrwrk

Details
Question 1297. And, do you agree or disagree: c. Family life often suffers because men concentrate too much on their work.

Overview
For further details see the official GSS documentation.

Counts by year:

year    iap agree   can't choose    disagree    neither agree nor disagree  no answer   strongly agree  strongly disagree   skipped on web  Total
1972    1613    -   -   -   -   -   -   -   -   1613
1973    1504    -   -   -   -   -   -   -   -   1504
1974    1484    -   -   -   -   -   -   -   -   1484
1975    1490    -   -   -   -   -   -   -   -   1490
1976    1499    -   -   -   -   -   -   -   -   1499
1977    1530    -   -   -   -   -   -   -   -   1530
1978    1532    -   -   -   -   -   -   -   -   1532
1980    1468    -   -   -   -   -   -   -   -   1468
1982    1860    -   -   -   -   -   -   -   -   1860
1983    1599    -   -   -   -   -   -   -   -   1599
1984    1473    -   -   -   -   -   -   -   -   1473
1985    1534    -   -   -   -   -   -   -   -   1534
1986    1470    -   -   -   -   -   -   -   -   1470
1987    1819    -   -   -   -   -   -   -   -   1819
1988    1481    -   -   -   -   -   -   -   -   1481
1989    1537    -   -   -   -   -   -   -   -   1537
1990    1372    -   -   -   -   -   -   -   -   1372
1991    1517    -   -   -   -   -   -   -   -   1517
1993    1606    -   -   -   -   -   -   -   -   1606
1994    1545    695 33  243 286 27  122 41  -   2992
1996    1444    825 16  198 169 1   230 21  -   2904
1998    2832    -   -   -   -   -   -   -   -   2832
2000    940 877 43  361 331 22  209 34  -   2817
2002    1857    415 6   264 108 -   99  16  -   2765
2004    1906    460 4   188 135 -   94  25  -   2812
2006    2518    945 14  477 304 1   208 43  -   4510
2008    694 653 12  310 161 -   143 50  -   2023
2010    614 662 6   388 192 3   122 57  -   2044
2012    672 558 11  382 170 -   130 51  -   1974
2014    863 702 7   479 234 1   176 76  -   2538
2016    979 819 9   536 257 -   171 96  -   2867
2018    789 644 11  475 220 2   134 73  -   2348
2021    1315    886 1   487 1001    -   202 138 2   4032
2022    1168    885 15  537 618 1   201 117 2   3544
2024    1126    787 19  481 611 -   195 89  1   3309
Total   50650   10813   207 5806    4797    58  2436    927 5   75699
Values
1 strongly agree

2 agree

3 neither agree nor disagree

4 disagree

5 strongly disagree

NA(d) can't choose

NA(i) iap

NA(j) I don't have a job

NA(m) dk, na, iap

NA(n) no answer

NA(p) not imputable

NA(r) refused

NA(s) skipped on web

NA(u) uncodeable

NA(x) not available in this release

NA(y) not available in this year

NA(z) see codebook

Source
General Social Survey https://gss.norc.org

[Package gssrdoc version 0.7.0 Index]

Variable name: meovrwrk
Variable label: Men hurt family when focus on work too much
1994 was the first year of the survey.
695 respondents agreed with the statement.
iap – missing. Values: the numeric and response category key (1 = strongly agree)

We can find which years one or more variables were asked with the gss_which_years() function.

gss_which_years(gss_all, meovrwrk)

# A tibble: 35 × 2
   year      meovrwrk
   <dbl+lbl> <lgl>   
 1 1972      FALSE   
 2 1973      FALSE   
 3 1974      FALSE   
 4 1975      FALSE   
 5 1976      FALSE   
 6 1977      FALSE   
 7 1978      FALSE   
 8 1980      FALSE   
 9 1982      FALSE   
10 1983      FALSE   
# ℹ 25 more rows

Heads Up!

If run in the console, to see all rows, wrap the code in the print() command: print(gss_which_years(gss_all, meovrwrk), n = 40)

Other Formats

Agenda

Learning objectives

R & RStudio Workflow

Replication

Steps in a workflow

File types

File names

RStudio projects

Filepaths

Tour: RStudio Panes

Tour recap: Panes

R-scripts and Quarto

Blank slate

Comprehensive R Archive Network (CRAN)

Packages

Support

Quarto

Quarto

RScript

Quarto

Documentation

Tour: Quarto

Tour recap: Quarto

Tour recap: Quarto Code-chunks

How will we use Quarto?

Getting Started

Create a RStudio Project

Your first code-along

Packages

Install here() and tidyverse()

Install gssr() and gssrdoc()

Load the packages

Environment

Project structure

here()

Example folder structure

Create a folder structure

Create sub-folders

Check your work

Save code-along

Meet your data

Load your data

Load GSS 2024

Browse dataframe

Codebook

Names

Variable documentation

Variable documentation example

Install `here()` and `tidyverse()`

Install `gssr()` and `gssrdoc()`

`here()`